In recent years, various kinds of efforts have been made for the purpose of efficient use of energy. In facilities such as buildings, efforts to change energy consumption patterns are being made with the purpose of complying with the Revised Energy Conservation Act, gaining LEED (Leadership in Energy and Environmental Design) accreditation, or the like. For example, there are operation changes such as changing a set temperature of air conditioning to achieve energy savings during time periods of large energy consumption, shifting the time to start working to early morning, or, not working overtime in winter because of significant energy consumption by air heating. By these operation changes or the like, it is possible to change the distribution of energy consumption patterns in the facility, and thereby an effect such as peak shift of electrical power or reduction in cost is able to be obtained.
If a relationship between the current status of operations and energy consumption volume of the facility is able to be grasped, effects of energy savings or the like with the operation changes or equipment changes can be quickly and exactly calculated by simulation, and accordingly, effective changes can be carried out. However, it is difficult to grasp the current status of operations. For example, in a building, it is difficult to grasp set temperatures of air conditionings belonging to each tenant, or the number of people during a predetermined time period on each floor. Even if sensing by motion sensors or the like is to be carried out, there is a significant cost to install the sensors. On the other hand, a method to predict energy consumption based on only simple information, such as a history related to energy or equipment information of the building lacks accuracy as compared to prediction by simulation. Therefore, it is required to estimate the current status of operations with high accuracy while keeping the costs low.